Original ā FGSM Attack ā Patch Attack ā Perturbation Magnified
š„ Gradient Heatmap Analysis
Shows how the model's attention shifts under adversarial attack
š Noise Intensity Analysis (FGSM)
Testing model robustness across different perturbation budgets (ε)
Perturbation (ε)
Predicted Class
Confidence
L2 Distortion
Status
ε = 0.01
285
41.06%
3.880
ā FOOLED
ε = 0.03
285
47.81%
11.639
ā FOOLED
ε = 0.05
285
47.96%
19.397
ā FOOLED
ε = 0.10
285
30.52%
38.729
ā FOOLED
ε = 0.20
285
5.62%
69.611
ā FOOLED
š Detailed Robustness Metrics
100%
Clean Accuracy
0.4643
Avg Confidence Change (FGSM)
12.1406
Avg Confidence Change (PGD)
7.93
Avg L2 Distortion (PGD)
š”ļø Defense Effectiveness
Testing defensive preprocessing against PGD attack
Defense Method
Confidence
Recovery Status
No Defense
11.66%
ā Still Fooled
Jpeg
26.36%
ā Still Fooled
Spatial Smoothing
4.09%
ā Still Fooled
š” Security Recommendations & Best Practices
Adversarial Training: Retrain the model on adversarial examples generated during this analysis. This is the most effective defense, improving robustness by 40-60% on average.
Input Preprocessing: Implement JPEG compression (quality 50-70) or spatial smoothing as a first line of defense. These are computationally cheap and can reduce attack success rate by 15-30%.
Gradient Masking: Use defensive distillation or gradient regularization to make gradient-based attacks less effective.
Ensemble Methods: Deploy multiple models with different architectures. An adversarial example crafted for one model is less likely to transfer to others.
Input Validation: Implement runtime anomaly detection to flag suspicious inputs before classification. Monitor for unusual activation patterns.
Rate Limiting: For API deployments, limit query rates to prevent attackers from probing model decision boundaries through iterative queries.
Certified Defenses: Consider randomized smoothing for provable robustness guarantees within a certified L2 radius.
š§ Technical Configuration
Framework
ARAVM (Adversarial Robustness Analyzer for Vision Models)